Platform management of integrated access of public and privately-accessible datasets utilizing federated query generation and query schema rewriting optimization
Various techniques are described for platform management of integrated access of public and privately-accessible datasets utilizing federated query generation and query schema rewriting optimization, including receiving at a dataset access platform a query formatted according to a first data schema, generating a copy of the query, saving the query and the copy to a datastore, parsing the copy of the query in the first schema using an inference engine, determining whether the query comprises data associated with an access control condition associated with accessing the dataset, the access control condition being configured to indicate whether the query is permitted to access the dataset, and rewriting, using a proxy server, the copy of the query in a second schema by converting the copy of the query into a triple associated with the query and another triple associated with the access control condition.
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This application is a continuation application of copending U.S. patent application Ser. No. 16/457,750, filed Jun. 28, 2019 and titled, “PLATFORM MANAGEMENT OF INTEGRATED ACCESS OF PUBLIC AND PRIVATELY-ACCESSIBLE DATASETS UTILIZING FEDERATED QUERY GENERATION AND QUERY SCHEMA REWRITING OPTIMIZATION,” U.S. patent application Ser. No. 16/457,750 is a continuation application of U.S. patent application Ser. No. 15/439,908, filed Feb. 22, 2017, now U.S. Pat. No. 10,452,975 and titled, “PLATFORM MANAGEMENT OF INTEGRATED ACCESS OF PUBLIC AND PRIVATELY-ACCESSIBLE DATASETS UTILIZING FEDERATED QUERY GENERATION AND QUERY SCHEMA REWRITING OPTIMIZATION,” U.S. patent application Ser. No. 15/439,908 is a continuation-in-part application of U.S. patent application Ser. No. 15/186,514, filed Jun. 19, 2016, now U.S. Pat. No. 10,102,258 and titled, “COLLABORATIVE DATASET CONSOLIDATION VIA DISTRIBUTED COMPUTER NETWORKS,” U.S. patent application Ser. No. 15/439,908 is also a continuation-in-part application of U.S. patent application Ser. No. 15/186,515, filed Jun. 19, 2016, now U.S. Pat. No. 10,515,085 and titled, “CONSOLIDATOR PLATFORM TO IMPLEMENT COLLABORATIVE DATASETS VIA DISTRIBUTED COMPUTER NETWORKS,” U.S. patent application Ser. No. 15/439,908 is also a continuation-in-part application of U.S. patent application Ser. No. 15/186,516, filed Jun. 19, 2016, now U.S. Pat. No. 10,452,677 and titled, “DATASET ANALYSIS AND DATASET ATTRIBUTE INFERENCING TO FORM COLLABORATIVE DATASETS,” U.S. patent application Ser. No. 15/439,908 is also a continuation-in-part application of U.S. patent application Ser. No. 15/186,517, which issued as U.S. Pat. No. 10,324,925, filed Jun. 19, 2016; U.S. patent application Ser. No. 15/439,908 is also a continuation-in-part application of U.S. Nonprovisional patent application Ser. No. 15/186,519, filed Jun. 19, 2016, now U.S. Pat. No. 10,699,027 and titled, “LOADING COLLABORATIVE DATASETS INTO DATA STORES FOR QUERIES VIA DISTRIBUTED COMPUTER NETWORKS,” U.S. patent application Ser. No. 15/439,908 is also a continuation-in-part application of U.S. patent application Ser. No. 15/186,520, filed Jun. 19, 2016, now U.S. Pat. No. 10,346,429 and titled, “MANAGEMENT OF COLLABORATIVE DATASETS VIA DISTRIBUTED COMPUTER NETWORKS,” all of which are hereby incorporated by reference in their entirety for all purposes. This application is also related to U.S. patent application Ser. No. 15/454,923 filed Mar. 9, 2017, now U.S. Pat. No. 10,353,911 and titled, “COMPUTERIZED TOOLS TO DISCOVER, FORM, AND ANALYZE DATASET INTERRELATIONS AMONG A SYSTEM OF NETWORKED COLLABORATIVE DATASETS,” U.S. Nonprovisional patent application Ser. No. 15/454,955 filed Mar. 9, 2017, now U.S. Pat. No. 10,691,710 and titled, “INTERACTIVE INTERFACES AS COMPUTERIZED TOOLS TO PRESENT SUMMARIZATION DATA OF DATASET ATTRIBUTES FOR COLLABORATIVE DATASETS,” U.S. patent application Ser. No. 15/454,969 filed Mar. 9, 2017, now U.S. Pat. No. 10,747,774 and titled, “INTERACTIVE INTERFACES TO PRESENT DATA ARRANGEMENT OVERVIEWS AND SUMMARIZED DATASET ATTRIBUTES FOR COLLABORATIVE DATASETS,” and U.S. patent application Ser. No. 15/454,981 filed Mar. 9, 2017, now U.S. Pat. No. 10,645,548 and titled, “COMPUTERIZED TOOL IMPLEMENTATION OF LAYERED DATA FILES TO DISCOVER, FORM, OR ANALYZE DATASET INTERRELATIONS OF NETWORKED COLLABORATIVE DATASETS,” all of which are also hereby incorporated by reference in their entirety for all purposes.
FIELDThe present invention relates generally to data science, machine and deep learning computer algorithms, data graph modeling, and analysis of linked data. More specifically, techniques for management of integrated access to public and privately-accessible datasets are described.
BACKGROUNDAs demand for data and data science expands rapidly, significant research into potential uses of data in various applications are also increasing at a dramatic rate. With enormous amounts of data and information becoming increasingly available, utilizing data is becoming a greater focus of both consumer and commercial activities alike. Datasets (i.e., sets or groups of logically-related data and/or information) are being created to provide statistical information that researchers are using to discover new innovations and applications in almost every aspect of contemporary life and lifestyles. However, utilizing data also involves addressing a growing problem, which includes identifying data, sources thereof, and managing the ever-increasing amount of data becoming available. Moreover, as the amount and complexity of data, datasets, databases, datastores and data storage facilities increase, the ability to identify, locate, retrieve, analyze, and present data in useful ways is also becoming increasingly difficult. Today, managing large amounts of data for useful purposes poses a significant problem for individual users, organizations, and entities alike. Conventional techniques are problematic in that these are neither capable nor configured to manage large scale problems such as providing integrated access to data that is both available on public resources as well as those that are hosted or stored on private (i.e., secure (i.e., requiring authentication or authorization before access is permitted)) data storage resources. More importantly, users are typically burdened by conventional techniques in that access to data often requires not only proficient, if not expert, knowledge of both computer programming languages commonly known and used by data researchers and scientists (e.g., Python™, or others), but knowledge of complex computer databases, datastores, data repositories, data warehouses, data and object schema, data modeling, graph modeling, graph data, linked data, and numerous other data science topics is also required. Queries executed to retrieve data using conventional techniques typically require knowledge of specific programming or formatting languages, which can limit the usability of data. Specifically, conventional techniques are problematic because these lack intrinsic knowledge or technical functionality to permit a user such as a data scientist to locate, manage, access, and execute queries to retrieve data from various disparate and often dissimilar data resources.
Thus, what is needed is a solution for managing consolidated, integrated access to public and/or privately-accessible (i.e., secure) data without the limitations of conventional techniques.
Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings:
Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims.
A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims and numerous alternatives, modifications, and equivalents are encompassed. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description.
As illustrated in exemplary topology 100, in some examples, dataset access platform 102 may be configured to access public and/or privately-accessible datasets that are hosted on one or more databases, some, all, or none of which may be hosted on data networks such as networks 108-112. As used herein, “dataset access platform,” “access platform,” and “platform” may be used interchangeably without limitation and, in some examples, refers to a computer program, software, firmware, circuitry, algorithms, logic, hardware, or a combination thereof in order to implement techniques (e.g., systems, processes, or the like) for providing integrated query, access, retrieval, and other data operations using public and private datasets. As shown in topology 100, platform 102 may be configured to access databases 104-106, 114-118, 122 and/or datastore 123 including databases 124-128 in order to execute a query to retrieve one or more datasets stored in these elements. Datasets may be retrieved by, for example, data scientists, researchers, or any other user who may be interested in querying and retrieving a dataset for a given purpose. Datasets may include any type, form, format, or amount of publicly-accessible sources of data such as those available from Data.Gov, the U.S. Department of Defense, oceanographic data from the National Oceanic and Atmospheric Administration (NOAA), as well as privately collected, curated, managed, and created datasets such as those found on corporate, non-profit, research, scientific, or academic data networks. Datasets may be retrieved from a large number of sources and, as used herein, are not intended to be limited to any specific type, source, or format of data. In some examples, network 108 may be a publicly-accessible data network that includes one or more databases such as databases 114-118.
In some examples, databases 104-106, 114-118, 122 and datastore 123 including databases 124-128 may be accessed or used by dataset access platform 102 using a “farm” or collection of graph database engines (see element 228 (
As shown, platform 102 may be configured to access datasets stored on publicly-accessible (i.e., public or open) databases 104-106 and 114-118 or, in some examples, private database 122 and/or datastore 123 and databases 124-128. Platform 102, in some examples, may be a platform or application such as that developed by Data.World of Austin, Texas, including various features and functionality, as described in some of those properties incorporated by reference as set forth above. As shown, datastore 123 includes databases 124-128, although the number, type, format, data schema, and other characteristics may be varied and are not limited to the examples shown and described. For example, datastore 123 may use a database management system (not shown) to manage databases 124-128. As shown here, platform 102 may be configured to communicate over one or more other data networks such as the Internet, a private data network, or a computing cloud, without limitation to the type of data network provided a layered topology is used to communicate queries to/from platform 102 and a destination or target database (e.g., databases 104-106, 114-118, 122 and datastore 123 including databases 124-128). Platform 102 may also be configured to access datastore 123, which could be housed and operated on a separate data network (e.g., data network 112) than another data network through which a query or request is transmitted, passed, or sent (e.g., data network 110). In other words, platform 102 may be a standalone, distributed, local, remote, or cloud-based application, process, algorithm(s), computer program, software, firmware, hardware, server, or the like (hereafter “application”) that may be a standalone or distributed application, the latter of which may have one or more resources housed, stored in memory, executed from, or reside on disparate physical resources (e.g., servers, computers, or the like) in different geographic locations. However, when a query or request to query (the terms “query,” “request,” or “request to query” may be used interchangeably herein) is received by platform 102 for one or more of databases 104-106, 114-118, 122 and datastore 123 including databases 124-128, platform 102 may be configured to receive, parse, interpret, convert, rewrite, optimize, and execute the query in order to retrieve a dataset from one of the aforementioned data sources (i.e., databases 104-106, 114-118, 122 and datastore 123 including databases 124-128).
In some examples, a query (e.g., sent in SQL, MySQL, R, XML, Python, or any other programming or formatting language that is used to generate and send queries for retrieving datasets) may be received by platform 102 and sent to access control module 120 (as with platform 102, access control module 120 may be a standalone, distributed, local, remote, or cloud-based application, process, algorithm(s), computer program, software, firmware, hardware, server, or the like (hereafter “application”)), which provides access control functionality and prevents unauthorized access to datasets stored on one or more of databases 122 and 124-128 and datastore 123. In other words, access control module 120 receives queries on behalf of, for example, a private data network (e.g., network 112), which could be a scientific, academic, research, governmental, military, financial, corporate, non-profit, or any other type of data network in which non-public access is desired or security measures including, but not limited to access control module 120, are intended to limit, deter, or prevent access. If the query received by platform 102 and sent to network 112, which is an exemplary private data network, is rejected due to a lack of authorization or permission to access the dataset and/or data network (i.e., an access control condition is not met), platform 102 can notify a user (not shown) on a display or user interface that indicates a status of the query (also not shown). For example, a query written in SQL may be received by platform 102, which may be a standalone (e.g., hosted, remote, or local) or distributed (e.g., server, network, or cloud-based) software platform composed of multiple programs or scripts (e.g., Java®, JavaScript®, and/or other programming or formatting languages, structured or unstructured, or the like) that is configured to parse and analyze the query to determine through inference (as described in greater detail below) attributes, one of which may include an access control condition that permits the query to be run (i.e., executed) against an access-controlled (e.g., password, encryption, authentication, token-based, or any other form of electronic or digital security measure intended to limit or prevent access to a given dataset) database, datastore, dataset, network, or the like. Once authenticated (i.e., an access control condition matches or is approved by access control module 120), a query (not shown) from platform 102 may be permitted access in order to retrieve a dataset from database 122 or datastore 123 (and, subsequently, databases 124-128). Due to conventional solutions being problematic in handling and executing queries in one format against databases that may be in another format, platform 102 is configured to receive, parse, and run inference operations (as described in greater detail below) in order to determine and identify any attributes that may be related to the query, the dataset(s), or the database or datastore in which the dataset(s) are stored. More specifically, platform 102 includes, among other modules and functionality, an inference engine (not shown) that is configured to infer one or more attributes of a query, the target dataset (i.e., the dataset requested once the query has been executed), and the source database or datastore on which the dataset(s) are stored. Further, platform 102 may also be configured to convert a query from one format (e.g., SQL or another structured or unstructured query language) into a different “atomic” format (e.g., RDF™ (as developed by W3C®, or another triple-oriented language (i.e., languages and protocols such as SPARQL™ (as also developed by W3C®) that may be used to convert data associated with queries into subject-predicate-object-oriented data structures otherwise known as “triples”) that can be used to generate, by platform 102, rewritten queries that incorporate other triple data directed to attributes such as type, format, access control conditions, or in an integrated manner against various types and formats of databases, datastores, data repositories, data warehouses, and the like.
As an example, platform 102 may be configured to rewrite a query (e.g., programmed or formatted in SQL, Python, R, or other statistical or data analytical software) from one format, structure, or schema to another in order to execute a query against multiple disparate types of data storage facilities (e.g., databases, datastores, data repositories, data warehouses, and the like), which may each be of a different schema, structure, and/or type, without restriction. Further, in some examples, platform 102 may be configured to rewrite a query from one format, structure, or schema into another, but also “optimize” a rewritten query (as described in further detail below), by converting data associated with one or more inferred attributes that were determined during the parsing of the query upon its receipt by platform 102. “Optimizing” a query before, during, or after it has been rewritten by platform 102, may, in some examples, refer to optimizing a copy of a query or a master of a query. Optimizing a query may occur during or after a rewriting operation has been performed by platform 102, which could include, but is not limited to, rewriting a query (i.e., master or a copy) from one query language to another format that can then be used to generate further downstream queries for different target or disparate databases that may include datasets that are either sought, in accordance with the original query, or logic incorporated into platform 102 may execute to infer there may be other datasets that are indexed or linked (i.e., as linked data) by platform 102 that, although not known or targeted by the original query, could be returned with the intended target dataset. In some examples, queries may be optimized after being written from SQL to triples using RDF™, SPARQL™, or the like because the rewritten triple data, which may be stored in a datastore accessed by platform 102, but intended to store converted triple data from incoming queries (i.e., a “triple store”) may be retrieved with other triple data that has been generated resultantly from inferred attributes. In other words, inferred attributes such as type, data types (i.e., specific types of data that are typically identified by columnar or row headings in a tabular format, but could also be found in a multi-dimensional grid storage structure such as name, date, value, postal code, country, state, or any other type that can be used to identify a logical grouping of data, without limitation or restriction), data structure, data schema, object schema, addresses (e.g., Uniform Resource Locator (URL), Uniform Resource Identifier (URI), web address, and the like), layout, design, style, format, language, structure, and others without limitation to any particular attribute or type or category thereof. The triple data rewritten from the query and the triple data associated with attributes related to the query (hereafter, “query” may refer to a copy of a query or a master (i.e., original or originally received by platform 102) query, without limitation or restriction) may be specifically rewritten for a database housing or storing the intended target dataset database. In some examples, an original query or a copy of an original query may be subject to various data operations by platform 102, without restriction or limitation. If a copy of an original query is used by platform 102, the original query may itself be identified as a “master” and saved to one or more of databases 104-106 or another database, datastore, data warehouse, data repository, or other data facility or structure used by platform 102 to store internal data. Thus, a master query or master (hereafter “master”) may be preserved in the event query data used by platform 102 becomes corrupted or unusable.
In some examples, other databases that are “known” through previous queries or discovery by platform 102 that may store or house datasets similar, related, or associated with the intended dataset may be identified as a linked dataset or linked data and included in part of a data model or graph that can be used to retrieve data or datasets in response to various queries. In other words, platform 102 may use a graph (i.e., data model) that, once a query is received, logic (e.g., a logic module that may employ rules, machine learning, artificial intelligence, deep learning, natural language processing, or other algorithms, software, computer programs, applications, or the like to implement decision-based processing of data) then determines other linked data may be related to the dataset sought by the query and delivered to the user in response. Further, the linked datasets may also be included in a modified or new graph that may be created to include the intended target dataset as a new node within the graph. Various types of graph generation techniques may be used, without limitation or restrictions, such as mapping different data types (e.g., using specification such as comma separated values (“csv”) to RDF, R2RML, among others) and storing these maps as graphs within a database or datastore (e.g., databases 104-106 and 114-118). Other graph generation techniques may be used and are not limited to any particular algorithm, process, or methodology.
In some examples, although a SQL-based query may have a SELECT statement (i.e., a programmatic query command or query statement intended to fetch an intended dataset or data stored within a given database), platform 102 may be configured to convert the query statement (e.g., SELECT in SQL, and other comparable commands in any other type of query language, structured or unstructured) intro triple data and, using the attributes, include other triple data that can be used to rewrite the query into a format, language, or structure that can be used to retrieve a dataset from a database, regardless of the database format, schema, structure, or language of the target database and dataset(s). Further, the triple data associated with attributes of the query may also be used to manage, navigate, address, respond to, or otherwise perform data operations at the target database that may be required before access to the target or intended dataset are permitted or authorized. For example, a password, token, hash value, or any other type of security-oriented attribute may be converted into one or more triples and, in some examples, an endpoint server (not shown) associated, in data communication, or configured to perform data operations with platform 102 may be used to rewrite the triple data of the query and the attribute into another form, format, language, structure, or schema for a target database that the endpoint server is configured to communicate with over one or more data networks. In some examples, platform 102 may be configured to receive a query, rewrite the data associated with the query and any attributes (e.g., attributes of the query, the target dataset(s), the target database(s), paths, linked data, or any other attribute including, but not limited to those examples provided above) into a language, structure, schema, or format associated with another database by converting query data (i.e., data associated with a query) and data associated with attributes of the queries into triples, execute the rewritten queries, and, in some examples, return not only the requested dataset(s), but also dataset(s) that may be related to the dataset(s). In other examples, platform 102 may be configured to return only the target dataset(s) requested by the query and no others. In still other examples, platform 102 may be configured to return some dataset(s) that may be associated with or related to the target dataset(s) requested by the query, which may be determined based on rules or logic of platform 102. Further, platform 102 may also be configured to create or modify a graph (e.g., data model) that is used when a query for a given dataset is received, which may be further used to return additional data that could be valuable due to an attribute-determined relationship or association between the target dataset, the query, and other dataset(s) known or graphed or identified as linked data by platform 102. The above-described topology, elements, and processes may be varied in size, shape, configuration, function, and implementation and are not limited to the examples shown and described.
As shown, application 201 may be a implemented as a process, computer program, software, firmware, hardware, circuitry, logic, or a combination thereof (hereafter “application”) and, in some examples, may be written in Java® and/or JavaScript®, among others. Each of elements 201-228 may be programmed, developed, or encoded using software programming techniques familiar to these programming and formatting languages or others, without restriction, regardless of whether object-oriented, structured, or unstructured. In some examples, application 201 is configured with elements 202-228 in order to receive query 203 that is directed to retrieve (e.g., fetch, download, access and copy, or otherwise obtain using one or more data operations) a target dataset (e.g., dataset 242) in response to rewritten query 244. As described herein, application 201 may be written in any programming or formatting language (e.g., SQL, Python, R, or others) used to query a database. Application 201 may be configured to receive query 203 using API 204 and analyzing, using logic module 210, query 203 to determine one or more attributes associated with query 203, dataset 242, or a database (e.g., databases 104-106, databases 114-118, database 122, and datastore 123 (including databases 124-128) as shown and described above in connection with
Here, in some examples, a replica of query 203 (not shown) or query 203 is parsed by logic module 210, which is configured to analyze data received by application 201 (e.g., query 203) or dataset 242 and to generate instructions to other elements within application 201 to perform various data operations such as those described herein. Structurally, logic module 210 may be a set of logical rules or algorithms for machine learning, deep learning, artificial intelligence, or the like. Logic module 210 may be programmatically coded in one or more languages such as Java®, JavaScript®, R, or others, without limitation or restriction. Functionally, logic module 210 may be configured to perform various data operations such as generating data or signals to provide instructions to inference engine 214, query engine 216, or any other element of application 201. Logic module 210 may also be configured to generate and send instructions (i.e., as data or signals) to graph database engine 228 in order to generate one or more data models associated with query 203. Further, during parsing, inference engine 214 may be configured to determine attributes associated with query 203 through inference (e.g., Bayesian, statistical, probabilistic, predictive, or other techniques may be employed for inference and are not limited to any specific types of techniques for inferring attribute data associated with query 203). In some examples, attributes may include, but are not limited to, any type of information or characteristic associated with or about a query, dataset 242, which is intended to be fetched by query 203 (i.e., using, for example, a SQL FETCH command to retrieve dataset 242 for a given database (not shown)), and the destination or target database from which dataset 242 is to be retrieved. While examples are provided for the disclosed techniques to operate on a singular dataset, these may also be extended to operate on multiple datasets and databases, without limitation or restriction. Attributes may include, but are not limited to, property attributes (e.g., string literal, numerical, or the like), values, qualities, characteristics, or any other data, metadata, and information about or related to an item contained within a dataset or a database and which can be inferred by inference engine 214. Attributes, once inferred by inference engine 214 as a result of parsing being directed by logic module 210, along with query 203 can be converted into “atomic” data or triples in accordance with languages, protocols, and formats such as the Resource Description Framework (hereafter “RDF”) as promulgated by the World Wide Web Consortium (hereafter “W3C”), SPARQL, and others used for organizing, formatting, programming, converting, structuring, or otherwise manipulating data for use on “semantic web” applications and the like, including semantic uses for retrieving dataset 242 from databases or the like or from other data networks that do not employ common data languages, formats, and protocols. By converting, for example, SQL-based data (or data for query 203 formatted using a structured or unstructured language) can be converted into RDF triple data that can be used as a common base language, format, or protocol that can later be used by query engine 216 and proxy/endpoint server 206 to “rewrite” or construct rewritten query 244, which is ultimately transmitted from application 201 to a database for retrieving dataset 242. In some examples, dataset 242 may be retrieved or fetched from a database using rewritten query 244 and may include not only dataset 242, but also other datasets that might be related to or are similar to the dataset sought.
In some examples, the determination of whether dataset 242 may be related to other dataset(s) that were previously retrieved or otherwise indexed by application 201 and its elements (namely, graph database engine 228, which may be configured to create a graph or data model representative of dataset 242 that were previously fetched (i.e., retrieved) and/or stored in one or more of databases 220-224) may be made by logic module 210, query engine 216, and graph database engine 228. When query 203 is received, for example, logic module 210 analyzes inferred attribute data from inference engine 214 and can generate/send instructions to query engine 216 to reference graph database engine 228 in order to determine whether any of the triple data converted from query 203 and stored in one or more of databases 220-224 matches previously converted triple data stored similarly. Alternatively, a graph created of query 203 (or a copy thereof) or dataset 242 may also be stored in one or more of databases 220-224 and used as a reference for a comparison to another graph previously stored in databases 220-224 to determine if there is a match (i.e., where there are other datasets that may be related (and presumably of interest to a data scientist (i.e., user)) or similarity with dataset 242. In other examples, a rule or set of rules that establish a percentage or numerical threshold may be input using logic module 210 (e.g., display module 218 may be configured to generate, by executing one or more scripts, forms, or formats such as HTML, XML, PUP, or the like) to provide a user interface that a data scientist or researcher (i.e., a user of platform 200) may use to input a rule, criteria, or restriction for use in determining whether there are any dataset(s) that may be similar to dataset 242. In still other examples, users may enter other rules, criteria, or restrictions that permit or do not permit application 201 to return similar or matching datasets for presentation on a user interface (not shown) provided by display module 218, which, working in concert with API, may receive and send (for display or visual rendering) data in various types of formats including, but not limited to HTML, XML, XHTML, or any other type of programming or formatting language that may be used to generate the user interface.
Referring back to inference engine 214, any attributes inferred may be analyzed by logic module 210 and then converted into, for example, triple data (e.g., triple formats such as those described herein and in accordance with protocols such as SPARQL, RDF, among others, without limitation and/or restriction) that can be stored along with the triple data associated with query 203 itself, stored, that is, in one or more of databases 220-224. Inference engine 214 may also be configured to infer attributes about a given dataset(s) such as layout (e.g., columns, rows, axes, matrices, cells, text, among others), data type (e.g., string literals, numbers, integers, fractions, decimals, whole numbers, and the like), but also exceptions (i.e., data that is inconsistent with inferred attributes or other data within a given dataset(s)). In some examples, when exceptions are found, display module 218 may be configured to visually present, render, or otherwise display, in various types of graphical user interface layouts (not shown), without limitation or restriction. In some examples, user interfaces may be presented that provide, in addition to data from a retrieved dataset(s), but also exceptions, annotations, outlier data, inferred attributes, attribute data, or others, using techniques that data scientists and researchers would be familiar with using (e.g., Python, R, and the like) without requiring in-depth or expert knowledge of programming languages underlying platform 102 (e.g., SPARQL, RDF, Java®, JavaScript®, among others). In some examples, one or more of databases 220-224 may be configured to store only triple data, while another database may be configured to store query 203 as a master (as previously described) or copies thereof in order to restore from a catastrophic loss or data corruption event. As an example, query 203 may be rejected by a target database (e.g., databases 104-106, databases 114-118, database 122, and datastore 123 (including databases 124-128) as shown and described above in connection with
In some examples, an access control condition, in some examples, as a type of attribute can also be converted by conversion module 212 into triple data that may be stored in one or more of databases 220-224, one or all of which may be either local, remote (not shown), or distributed (local or remote) data storage facilities. In some examples, databases 220-224 may be standalone, server, network, or cloud-based data storage facilities and are not limited to the examples or configurations shown and described in connection with
Referring back to conversion module 212, data associated with query 203 (or a copy thereof) may be converted into triple data and stored in one or more of databases 220-224, which may be later used to generate rewritten query 244 by, in some examples, proxy/endpoint server 206. In some examples, proxy/endpoint server 206 may be implemented using multiple instantiations for different types, structures, formats, and data schema of databases, datastores, data warehouses, data repositories, or any other types of data storage facility(s). As shown, after query 203 has been converted into triple data that may be stored in one or more of databases 220-224 (and as further described above) and any inferred attributes determined by inference engine 214 have also been converted into triple data (which may likewise be stored in one or more of databases 220-224), proxy/endpoint server 206 and query engine 216 are configured to generate rewritten query 244 for each target database (not shown) on which dataset 242 is stored (e.g., as originally programmed using, for example, a FETCH statement in SQL) as well as any other dataset(s) that have been identified by logic module 210 as a result of analyzing graphs and/or data models generated by graph database engine 228 and/or those previously generated by graph database engine 228 and stored on one or more of databases 220-224 (i.e., identifying other datasets that may be similar to or match dataset 242, or identifying isomorphic (i.e., data that is related to other data) amongst queried, retrieved, or linked dataset(s)). Further, logic module 210 may also limit, expand, or otherwise modify the number and type of dataset(s) retrieved in response to a fetch command or statement, depending upon rules or instructions provided by a user as received by API 204 and display module 218. In still further examples, proxy/endpoint server 206 may include multiple instantiations, each of which is configured to generate multiple rewritten queries for different types, formats, structures, and/or data schemas for various databases (i.e., multiple versions of rewritten query 244, where each version may be generated for different types of databases (e.g., SQL, MySQL, PHP, XML, or others), without limitation or restriction to any particular type, format, or data schema of database. The described techniques enable data scientists (e.g., users) to generate a request using a query language that can be parsed, analyzed, converted, and rewritten in order to support different types, formats, structures, and data schemas without having to manually rewrite each query for a specific type of database. Further, rewritten query 244 may be “optimized” such that data or metadata representing attributes inferred by inference engine 214 can also be included as triple data during the rewriting process (as described in further detail below) in order to include data or information that can not only fetch or retrieve dataset 242, but also dataset(s) that may be useful, valuable, or otherwise related to the one sought by query 203. Optimization may also include rewriting query 203 from one query language into triples, as discussed herein, and from the triples data into rewritten query 244 by proxy/endpoint server 206, which may also include, during the rewriting process (as described in greater detail below) an access control condition (e.g., password, token, authentication data, encryption data, hash value, or other security data or information) from the converted triple data stored in databases 220-224 in order for rewritten query 24 to gain access to and retrieve from, for example, dataset 242 from a private (i.e., secure) network (e.g., network 112, which may include access control module 120, datastore 123, and databases 122-128). In other examples, the above-described elements may be varied in size, shape, configuration, function, and implementation and are not limited to the descriptions provided.
As shown, stack 300 includes application layer 302, which may be the architectural layer at which application 201 (
Query layer 304 is an exemplary layer of the architecture of application stack 300, which may be an architectural layer at which query data is retrieved, analyzed, parsed, or otherwise used to transfer data for various computing operations associated with receiving query 203 (
Here, linked data layer 306 may be an architectural data layer at which query 203 (
Here, triple data layer 308 is illustrative of an exemplary layer in the architecture of application 201 (
In some examples, query copy/replication 510 may be a process that is implemented by application 201 (
When a replica is generated by query copy/replication 510, in some examples, storage 512 may be configured to run or execute as a process to store a generated copy of a query and the original query (i.e., master) in one or more databases associated with application 201 (
Here, triple conversion 514 may be implemented as, for example, a process configured to convert query data into triples (e.g., RDF triples, items that are subject-predicate-object oriented, or another atomic format apart from those described herein). Data associated with a query may include query data received and parsed directly from, for example, query 203 (
Here, some data networks may utilize SQL as a primary data storage and query language while others may use DMX for data mining purposes, and still others may use LDAP for querying services run over Transport Control Protocol/Internet Protocol (i.e., “TCP/IP”). In still other examples, proxy/endpoint server 206 may use different query languages and the processes described herein such as triple conversion 514, endpoint query generation 516, and endpoint query execution 518 are not limited to any particular language or version thereof. In other examples, the above-described processes may be designed, implemented, configured, or otherwise executed differently and are not limited to the examples shown and described.
Alternatively, in some examples, if an access control condition (e.g., such as those described above) is determined by inference engine 214 (
Alternative processes may be implemented other than the examples shown and/or described. For example, an alternative process may be included to parse a query to identify its various components and then determine what datasets are desired (i.e., targeted) for access. Once determined, the targeted dataset(s) can be evaluated further by inferring any attributes such as access control conditions. Access control conditions inferred may include, but are not limited to, checking token-based access controls for each targeted dataset and, if an access control condition or attribute indicates access is not authorized by data within the query, it is rejected and data is transmitted back to the user for display via, for example, display module 218 (
In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions.
Running as parallel processes to those used for handling query copies as described above, in some examples, a query may be identified as a master (630). Once identified, a database or datastore in data communication with application 201 (
Referring back to
Referring back to
In other examples, a public dataset may be stored on a public network and, if no access control condition is required, then platform 102 (
Referring back to
Referring back to
According to some examples, computer system 800 performs specific operations by processor 804 executing one or more sequences of one or more instructions stored in system memory 806. Such instructions may be read into system memory 806 from another computer readable medium, such as static storage device 808 or disk drive 810. In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation.
The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor 804 for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as disk drive 810. Volatile media includes dynamic memory, such as system memory 806.
Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read.
Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus 802 for transmitting a computer data signal.
In some examples, execution of the sequences of instructions may be performed by a single computer system 800. According to some examples, two or more computer systems 800 coupled by communication link 820 (e.g., LAN, PSTN, or wireless network) may perform the sequence of instructions in coordination with one another. Computer system 800 may transmit and receive messages, data, and instructions, including program, i.e., application code, through communication link 820 and communication interface 812. Received program code may be executed by processor 804 as it is received, and/or stored in disk drive 810, or other non-volatile storage for later execution. In other examples, the above-described techniques may be implemented differently in design, function, and/or structure and are not intended to be limited to the examples described and/or shown in the drawings.
Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.
Claims
1. A method, comprising:
- causing to deploy via a network data representing one or more portions of an application distributed among computing cloud-based resources, the one or more portions of the application configured to generate one or more federated queries; and
- identifying via the network a request to perform a query received in a first data format into at least one of the one or more portions of the application to access a dataset, the one or more portions of the application configured to perform data operations including: generating a copy of the query to form data representing a query copy in the first data format; storing either the query or the query copy, or both, to one or more data stores of the computing cloud-based resources; inferring an attribute associated with the query to form an inferred attribute; parsing either the query or the query copy, or both, to identify the inferred attribute associated with the query or the query copy; rewriting the query copy to convert into a second data format to form a rewritten query in the second data format; and modifying a graph to form one or more data links between the dataset and another dataset based on data representing the rewritten query.
2. The method of claim 1, further comprising:
- directing the query to one or more endpoints associated with a dataset access platform to retrieve query results associated with a target database configured to store the dataset as graph-based data.
3. The method of claim 1, further comprising:
- rewriting of the query copy to generate a federated query that is configured to access one or more endpoints.
4. The method of claim 1, wherein data representing the inferred attribute is a type of attribute including access control data.
5. The method of claim 1, wherein data representing the query includes data representing a security-related attribute as access control data.
6. The method of claim 1, wherein parsing either the query or the query copy, or both, comprises:
- determining the query comprises other data including authentication data to access the dataset.
7. The method of claim 1, wherein the first data format is associated with either a structure or an unstructured data schema, or both.
8. The method of claim 1, wherein the first data format is associated with a relational data schema.
9. The method of claim 1, wherein the first data format is associated with a schema compatible with a structured query language (“SQL”) or equivalent thereto.
10. The method of claim 1, wherein the second data format is associated with a triples-based format.
11. The method of claim 1, wherein the second data format is associated with a resource description framework (“RDF”) format.
12. The method of claim 1, wherein the query is a master query.
13. The method of claim 1, further comprising:
- receiving the query into at least a portion of a dataset access platform implemented in the one or more portions of the application.
14. The method of claim 1, further comprising:
- rewriting the query copy at least a portion of a proxy server implemented in the one or more portions of the application.
15. The method of claim 1, further comprising:
- retrieving query results from a target database configured to store the dataset as graph-based data.
16. A system comprising:
- a memory including executable instructions; and
- a processor configured to execute the instructions to:
- cause to deploy via a network data representing one or more portions of an application distributed among computing cloud-based resources, the one or more portions of the application configured to generate one or more federated queries; and
- identify via the network a request to perform a query received in a first data format into at least one of the one or more portions of the application to access a dataset, the one or more portions of the application configured to perform data operations including: generating a copy of the query to form data representing a query copy in the first data format; storing either the query or the query copy, or both, to one or more data stores of the computing cloud-based resources; inferring an attribute associated with the query to form an inferred attribute; parsing either the query or the query copy, or both, to identify the inferred attribute associated with the query or the query copy; rewriting the query copy to convert into a second data format to form a rewritten query in the second data format; and modifying a graph to form one or more data links between the dataset and another dataset based on data representing the rewritten query.
17. The system of claim 16, wherein the processor is further configured to:
- direct the query to one or more endpoints associated with a dataset access platform to retrieve query results associated with a target database configured to store the dataset as graph-based data.
18. The system of claim 16, wherein the processor is further configured to:
- rewrite of the query copy to generate a federated query that is configured to access one or more endpoints.
19. The system of claim 16, wherein data representing the inferred attribute is a type of attribute including access control data.
20. The system of claim 16, wherein the processor configured to parse either the query or the query copy, or both, is configured further to:
- determine the query comprises other data including authentication data to access the dataset.
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Type: Grant
Filed: May 31, 2022
Date of Patent: Mar 12, 2024
Patent Publication Number: 20220366252
Assignee: data.world, Inc. (Austin, TX)
Inventors: Bryon Kristen Jacob (Austin, TX), David Lee Griffith (Austin, TX), Triet Minh Le (Austin, TX), Shad William Reynolds (Austin, TX), Arthur Albert Keen (Austin, TX)
Primary Examiner: Joshua Bullock
Application Number: 17/828,257
International Classification: G06F 16/242 (20190101); G06F 16/21 (20190101); G06F 16/2453 (20190101); G06F 16/25 (20190101); G06F 16/901 (20190101); G06F 21/62 (20130101); G06N 3/08 (20230101); G06N 5/022 (20230101); G06N 5/04 (20230101);
